(Looking) Back to the Future: using space-time patterns to better predict the location of street crime

Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation

Spatial and Temporal Analysis of SARS-CoV-2 in Sewer Network in Reno-Sparks Metropolitan Area

COVID-19 disease, caused by SARS-COV-2 virus, strained public health entities and communities, which faced unprecedented challenges in implementing clinical testing to monitor and inform public on the spread of the disease. In the early stages of the pandemic, researchers indicted that the virus is present in wastewater and infected individuals shed the genetic material of SARS-CoV-2 virus in their feces. Researchers all around the world implemented wastewater-based epidemiology (WBE) as a tool to monitor wastewater, which provides an efficient pooled community sample and may predict COVID-19 occurrence. Even though wastewater surveillance has been in practice for decades, the novel area of WBE research for COVID-19 is based on the exploration of the potential to provide an integrated, community-level indication of the presence of COVID-19. In this study, we implemented WBE with geospatial analysis using Geographic Information System (GIS). The study also identified statistically significant spatial patterns of SARS-CoV-2 in wastewater through spatial sampling strategy across neighborhood-scale sewershed catchments in the Truckee Meadows Water Reclamation Facility service area. Using GIS technology of local spatial autocorrelation and directional distribution methods, wastewater surveillance at a more granular level provided greater sensitivity for detecting clusters, outlier, hot spots, and cold spots through the sampling campaign of sewer network

Methods used in the spatial analysis of tuberculosis epidemiology: a systematic review

Background: Tuberculosis (TB) transmission often occurs within a household or community, leading to heterogeneous spatial patterns. However, apparent spatial clustering of TB could reflect ongoing transmission or co-location of risk factors and can vary considerably depending on the type of data available, the analysis methods employed and the dynamics of the underlying population. Thus, we aimed to review methodological approaches used in the spatial analysis of TB burden. Methods: We conducted a systematic literature search of spatial studies of TB published in English using Medline, Embase, PsycInfo, Scopus and Web of Science databases with no date restriction from inception to 15 February 2017. The protocol for this systematic review was prospectively registered with PROSPERO (CRD42016036655). Results: We identified 168 eligible studies with spatial methods used to describe the spatial distribution (n = 154), spatial clusters (n = 73), predictors of spatial patterns (n = 64), the role of congregate settings (n = 3) and the household (n = 2) on TB transmission. Molecular techniques combined with geospatial methods were used by 25 studies to compare the role of transmission to reactivation as a driver of TB spatial distribution, finding that geospatial hotspots are not necessarily areas of recent transmission. Almost all studies used notification data for spatial analysis (161 of 168), although none accounted for undetected cases. The most common data visualisation technique was notification rate mapping, and the use of smoothing techniques was uncommon. Spatial clusters were identified using a range of methods, with the most commonly employed being Kulldorff's spatial scan statistic followed by local Moran's I and Getis and Ord's local Gi(d) tests. In the 11 papers that compared two such methods using a single dataset, the clustering patterns identified were often inconsistent. Classical regression models that did not account for spatial dependence were commonly used to predict spatial TB risk. In all included studies, TB showed a heterogeneous spatial pattern at each geographic resolution level examined. Conclusions: A range of spatial analysis methodologies has been employed in divergent contexts, with all studies demonstrating significant heterogeneity in spatial TB distribution. Future studies are needed to define the optimal method for each context and should account for unreported cases when using notification data where possible. Future studies combining genotypic and geospatial techniques with epidemiologically linked cases have the potential to provide further insights and improve TB control

Opportunity and Rationality as an Explanation for Suspicious Vehicle Fires: Demonstrating the Relevance of Time, Place, and Economic Factors

Opportunity theories of crime emphasize the non-random spatial and temporal patterning of criminal events. Such theoretical development has proven useful when extended beyond traditional applications to crime event data. This study continues to explore the wider utility of such criminological theories by examining the spatio-temporal patterns of vehicle fires through an opportunity lens. Specifically, we explore the patterns associated with different types of vehicle fires, and consider longitudinal socio-economic trends that may influence the perceived costs and benefits associated with crimes committed with the intention of escaping debt, such as vehicle arson. Data for this study were obtained from Surrey Fire Services (2000–2015) and contain information about all vehicle fires occurring in Surrey, British Columbia (BC), Canada. Post-fire incident reports were used to group the fire data into non-suspicious and suspicious categories. Both categories were analyzed for local and global spatial clustering, hourly, daily, and monthly temporal patterns, and changes over the study period. Findings indicate that suspicious vehicle fire events concentrate in both space and time, and these patterns are distinct from non-suspicious vehicle fires. Further, suspicious vehicle fires events are significantly related to unemployment and interest rates, whilst non-suspicious vehicle fires are not. These results demonstrate the relevance of opportunity theories of crime to understanding vehicle fire patterns. By extension, this provides an important opportunity to connect such patterns with targeted crime (and fire) prevention policy and practice

The role of functional urban areas in the spread of COVID-19 Omicron (Northern Spain)

This study focuses on the space-time patterns of the COVID-19 Omicron wave at a regional scale, using municipal data. We analyze the Basque Country and Cantabria, two adjacent regions in the north of Spain, which between them numbered 491,816 confirmed cases in their 358 municipalities from 15th November 2021 to 31st March 2022. The study seeks to determine the role of functional urban areas (FUAs) in the spread of the Omicron variant of the virus, using ESRI Technology (ArcGIS Pro) and applying intelligence location methods such as 3D-bins and emerging hot spots. Those methods help identify trends and types of problem area, such as hot spots, at municipal level. The results demonstrate that FUAs do not contain an over-concentration of COVID-19 cases, as their location coefficient is under 1.0 in relation to population. Nevertheless, FUAs do have an important role as drivers of spread in the upward curve of the Omicron wave. Significant hot spot patterns are found in 85.0% of FUA area, where 98.9% of FUA cases occur. The distribution of cases shows a spatially stationary linear correlation linked to demographically progressive areas (densely populated, young profile, and with more children per woman) which are well connected by highways and railroads. Based on this research, the proposed GIS methodology can be adapted to other case studies. Considering geo-prevention and WHO Health in All Policies approaches, the research findings reveal spatial patterns that can help policymakers in tackling the pandemic in future waves as society learns to live with the virus.This research was funded by the research project INNVAL20/03 (IDIVAL) entitled “Test de estrés o resistencia en el Sistema Cántabro de Salud, desarrollo de tecnologías innovadoras digitales para modelizar escenarios de mayor utilización sanitaria y soluciones de impacto socioeconómico y humano frente a la COVID-19.

Patterns and drivers of long term spatio-temporal change in a rural savanna landscape

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science 17th August 2015 in Johannesburg, South AfricaEcosystem services provide a vital lifeline to millions of people living in rural areas. The poorest people in these areas depend upon the natural resource base in their surroundings to provide these services. With growing populations in rural areas of South Africa, the natural resource base is under considerable pressure; however, uncovering the dynamics of vegetation in these systems has proven difficult. While much attention has been given to savanna ecology, long term studies on the patterns and drivers of woody biomass are few. We used 65 years of aerial imagery (from 1944 to 2009) over 31 953 ha of rural savanna in a communal rangeland in South Africa to determine the abundance of woody canopy cover. This data were captured at hectare resolution, giving a fine enough level of detail for local level analysis. We also captured data for five potential drivers for change at this resolution, in order to analyse these drivers for their relative importance in determining woody canopy cover throughout the study period. Surprisingly, while individual sites showed varied trends in the amounts of woody canopy cover through time, when pooled across all sites the total woody canopy cover increased over the 65 year period. Disturbance gradients were found around some of the villages, but only in 2009, suggesting that the drivers of disturbance gradients in these systems may have only operated sufficiently to produce disturbance gradients in recent years. A hot spot analysis (hot spots indicate cells that have similarly high values beyond what would be expected in a random distribution, with cold spots indicating the inverse) revealed an increase in both hot and cold spots through time, but with a low persistence of both through time. High canopy cover cells are presumed to be the result of bush encroachment, while low canopy cover cells are presumed to be the result of harvesting of trees for fuelwood or clearing for fields. The low persistence of hot and cold spots points to a system in continual change, with patches of hot and cold spots appearing and disappearing, and therefore drivers of change operating in short periods of time. MAP (Mean Annual Precipitation), and not an anthropogenic driver, was found to be the most important driver for woody canopy cover throughout the study period, with MAP up to 670 mm having a predictable pattern of hot and cold spots through time. Higher MAP was shown to have a non-linear and unpredictable pattern of hot and cold spots through time, indicating that low precipitation may produce a system where woody canopy cover is less influenced by other drivers and is more stable when acted upon by other drivers. This research demonstrates the value of a long term dataset, and the applicability of our methods for monitoring woody canopy cover. As such, it may well serve as a baseline for woody canopy cover in communal savanna rangeland systems, with the methodology employed here suitable for an early warning detection system for sudden changes in the woody canopy cover

Cluster recognition in spatial-temporal sequences: the case of forest fires

Forest fire sequences can be modelled as a stochastic point process where events are characterized by their spatial locations and occurrence in time. Cluster analysis permits the detection of the space/time pattern distribution of forest fires. These analyses are useful to assist fire-managers in identifying risk areas, implementing preventive measures and conducting strategies for an efficient distribution of the firefighting resources. This paper aims to identify hot spots in forest fire sequences by means of the space-time scan statistics permutation model (STSSP) and a geographical information system (GIS) for data and results visualization. The scan statistical methodology uses a scanning window, which moves across space and time, detecting local excesses of events in specific areas over a certain period of time. Finally, the statistical significance of each cluster is evaluated through Monte Carlo hypothesis testing. The case study is the forest fires registered by the Forest Service in Canton Ticino (Switzerland) from 1969 to 2008. This dataset consists of geo-referenced single events including the location of the ignition points and additional information. The data were aggregated into three sub-periods (considering important preventive legal dispositions) and two main ignition-causes (lightning and anthropogenic causes). Results revealed that forest fire events in Ticino are mainly clustered in the southern region where most of the population is settled. Our analysis uncovered local hot spots arising from extemporaneous arson activities. Results regarding the naturally-caused fires (lightning fires) disclosed two clusters detected in the northern mountainous are

Exploring spatiotemporal dynamics of urban fires: A case of Nanjing, China

Urban fire occurs within the built environment, usually involving casualties and economic losses, and affects individuals and socioeconomic activities in the surrounding neighborhoods. A good understanding of the spatiotemporal dynamics of fire incidents can offer insights into potential determinants of various fire events, therefore enabling better fire risk estimation which can assist with future allocation of prevention resources and strategic planning of mitigation programs. Using a twelve-year (2002–2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatiotemporal dynamics of urban fires using a range of exploratory spatial data analysis (ESDA) approaches. Of particular interest here are the fire incidents involving residential properties and local facilities due to their relatively higher occurrence frequencies. The results indicate that the overall amount of urban fires has greatly increased in the last decade and the spatiotemporal distribution of fire events varies among different incident types. The identified spatiotemporal patterns of urban fires in Nanjing can be linked to the urban development strategies and how they have been reflected in reality in recent years